Volume 6, Issue 4

In this paper, a KELM-based ensemble learning approach, integrating Granger causality test, grey relational analysis and KELM (Kernel Extreme Learning Machine), is proposed for the exchange rate forecasting. The study uses a set of sixteen macroeconomic variables including, import, export, foreign exchange reserves, etc. Furthermore, the selected variables are ranked and then three of them, which have the highest degrees of relevance with the exchange rate, are filtered out by Granger causality test and the grey relational analysis, to represent the domestic situation. Then, based on the domestic situation, KELM is utilized for medium-term RMB/USD forecasting. The empirical results show that the proposed KELM-based ensemble learning approach outperforms all other benchmark models in different forecasting horizons, which implies that the KELM-based ensemble learning approach is a powerful learning approach for exchange rates forecasting.

The article is about solving the last mile delivery problem in rural town or village. We want to test the drone’s potential in parcel delivery. The objectives are 1) to introduce the cluster and truck-drone in tandem delivery method, 2) to compare the new method with the traditional TSP method in aspect of truck running distance, energy using and time occupation. The parcel delivery demand is sparse, so it is not dense enough for a truck to carry on delivery. We try to identify the best route for the drone to deliver the goods. We use k -mean method to carry on clustering, then we use enumeration method to fulfill the centroids delivery, which comes from the depot. We design a model and calculate the energy, time and distance saving between drone using method (DTSP) and traditional TSP method. The drone attended delivery saves truck delivery distance, energy consumption and time. The truck running distance of DTSP method saves 91.87%, the truck running distance is shortened from 189.69 km to 15.4252 km. The DTSP method saves 90.45% of energy. The DTSP method brings a 29.75% cutoff in time aspect when there are two drone in running. The research introduces the cluster and TSP combination method, which is a good way to carry on last mile delivery. The result shows a bright future for drone to attend parcel delivery. The e-commerce corporation can apply this method in practice.

Based on the concept of productive capital stock, this paper estimated capital input by three asset types of China’s 36 service industries in 2003–2015, and compared with the results of wealth capital stock. This study found that the wealth capital stock method underestimates the actual capital input in each sector in varying degrees, and it may interference the accuracy of productivity evaluation in sectors. According to the new estimation results of capital input, this paper further applied four stages bootstrap-DEA method to estimate industrial productivity, and calculated its confidence intervals. This study found that, the years of education and the average wage have a significant positive impact on the productivity of service industries; the productive services have a short board effect in the whole service industry.

In the analysis on economic growth factors, researchers usually use the production function model to calculate and measure influencing factors’ contribution rates to economic growth. Common production functions include the CD (Cobb-Douglas) production function, the CES (Constant Elasticity of Substitution) production function, the VES (Variable Elasticity of Substitution) production function, and so on. In consideration of the diversity and complementarity of models, the paper combines the CD production function with the CES production function and then proposes a mixed production function. With regard to the parameter estimation of model, the paper gives an improved firefly algorithm with the high precision and a fast rate of convergence. With regard to the calculation of factors’ contribution rates, traditional methods generally have big errors and are not applicable to complicated models, so the paper offers a new method which can calculate contribution rates scientifically. Finally, the paper calculates the contribution rates of factors affecting Chinese economic growth and gets a good result.

This paper considers a discrete-time Geo/G/1 queue in a multi-phase service environment, where the system is subject to disastrous breakdowns, causing all present customers to leave the system simultaneously. At a failure epoch, the server abandons the service and the system undergoes a repair period. After the system is repaired, it jumps to operative phase i with probability q i , i = 1, 2 ⋯, n . Using the supplementary variable technique, we obtain the distribution for the stationary queue length at the arbitrary epoch, which are then used for the computation of other performance measures. In addition, we derive the expected length of a cycle time, the generating function of the sojourn time of an arbitrary customer, and the generating function of the server’s working time in a cycle. We also give the relationship between the discrete-time queueing system to its continuous-time counterpart. Finally, some examples and numerical results are presented.

An efficient method for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of out-leaving links among nodes bridging over communities. A new method for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this method, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition method, out-leaving links and inner links are processed separately. The method was used on empirical networks constructed from online social networks, and results indicated that this method is more accurate. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the method remained robust even when there were errors in the structure of the network.

Although the grey forecasting model has been successfully adopted in various fields and demonstrated promising results, the literatures show its performance could be further improved, such as for the DGM(1,1) model, based on a concave sequence, the modeling error will be larger. In this paper, firstly the definition of sequence convexity is given out, and it is proved that the output sequence of DGM(1,1) model is a convex sequence. Next, the residual change law of DGM(1,1) model based on the concave sequence is discussed, and the non-equidistance DGM(1,1) model is proposed. Finally, by introducing the symmetry transformation, a concave sequence is transformed into a convex sequence, called the symmetric sequence of the concave sequence, and then construct the non-equidistance DGM(1,1) model based on the convex sequence. The example results show that the novel method is more accurate than the direct modeling for a concave sequence.